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Record W2143985241 · doi:10.1109/nafips.2004.1336252

Rough set approximations in formal concept analysis

2004· article· en· W2143985241 on OpenAlex
Yiyu Yao, Yaohua Chen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04. · 2004
Typearticle
Languageen
FieldComputer Science
TopicRough Sets and Fuzzy Logic
Canadian institutionsUniversity of Regina
FundersSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsRough setFormal concept analysisSet (abstract data type)Approximations of πComputer scienceUniversal setMathematicsSet theoryDominance-based rough set approachApproximation theoryLattice (music)Algebra over a fieldDiscrete mathematicsTheoretical computer scienceAlgorithmArtificial intelligenceApplied mathematicsPure mathematics

Abstract

fetched live from OpenAlex

An important topic of rough set theory is the approximation of undefinable sets or concepts through definable sets. It involves the construction of a system of definable sets and the definition of approximation operators. In this paper, the notion of rough set approximations is introduced into formal concept analysis. Approximation operators are defined based on both lattice-theoretic and set-theoretic operators. The results provide a better understanding of data analysis using rough set theory and formal concept analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.007
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.247
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it